The ‘Impala-an Open Source SQL Engine for Hadoop’ is an ideal course package for individuals who want to understand the basic concepts of Massively Parallel Processing or MPP SQL query engine that runs on Apache Hadoop. On completing this course, learners will be able to interpret the role of Impala in the Big Data Ecosystem.
The course focuses on the basics of Impala. It further provides an overview of the superior performance of Impala, against other popular SQL-on-Hadoop systems.
Price (*ask for discount) 150 USD
Access Period 180 days
There are no prerequisites for this course.
- Data scientists
- Hadoop administrator and developers
- SQL developers
- Data warehouse developers
- Database administrators and developers
What is included
- 5 hours of self-paced video.
- Includes 6 high-quality demos covering important topics.
- Includes 1 Impala simulation exam.
- Includes 12 chapter-end quizzes and downloadable e-book.
- Course completion certificate.
- How To Earn?
Complete 85% of the course. Complete 1 simulation test with a minimum score of 60%.
- How To Maintain?
Certification Exam Format
- You should pay the online course fee then the online course access will be granted to you within 1 week after receiving payment.
- Course fee payment is not refundable.
Frequently Asked Questions
Introduction to Impala
- What is Impala
- Benefits of Impala
- Exploratory Business Intelligence
- Impala Installation
- Demo - Using Cloudera Manager for Impala
- Starting and Stopping Impala
- Demo - Starting Impala from Command Line
- Data Storage
- Managing Metadata
- Controlling Access to Data
- Impala Shell Commands and Interface
- Demo - Launching Impala Shell and Shell Command
Querying with Hive and Impala
- SQL Language Statements
- DDL Statements
- DML Statements
- CREATE DATABASE
- CREATE TABLE
- CREATE TABLE - Examples
- Internal and External Tables
- Loading Data into Impala Table
- ALTER TABLE
- DROP TABLE
- DROP DATABASE
- DESCRIBE Statement
- EXPLAIN Statement
- SHOW TABLE Statement
- INSERT Statement
- INSERT Statement - Examples
- SELECT Statement
- Data Type
- CREATE VIEW in Impala
- Hive and Impala Query Syntax
- Demo - Using Impala Shell for DDL and DDML SQL Statements
Data Storage and File Format
- Partitioning Tables
- SQL Statements for Partitioned Tables
- File Format and Performance Considerations
- Choosing File Type and Compression Technique
- Demo - File Formats and Compression Techniques
Working with Impala
- Impala Architecture
- Impala Daemon
- Impala Statestore
- Impala Catalog Service
- Query Execution Flow in Impala
- User - Defined Functions
- Hive UDFs with Impala
- Demo - UDF in Impala
- Improving Impala Performance